Webinar: Φ-Space for continuous phenotyping of single-cell multi-omics data

We have developed a new PLS method for cell type continuous annotation of single cells, now in preprint!

  • Φ-Space addresses numerous challenges faced by state-of-the-art automated annotation methods:
    • to identify continuous and out-of-reference cell states,
    • to deal with batch effects in reference,
    • to utilise bulk references and multi-omic references.
  • Φ-Space uses soft classification to phenotype cells on a continuum. The continuous annotation, or phenotype space embedding is then used to reduce the dimensionality of the data for various downstream analyses.

Φ-Space: Continuous phenotyping of single-cell multi-omics data. Jiadong Mao, Yidi Deng, Kim-Anh Lê Cao. bioRxiv 2024. 

View this 52min video of Kim-Anh Lê Cao presenting Φ-Space at the WEHI Bioinformatics seminar:

Abstract

Single-cell multi-omics technologies have empowered increasingly refined characterisa- tion of the heterogeneity of cell populations. Automated cell type annotation methods have been developed to transfer cell type labels from well-annotated reference datasets to emerging query datasets. However, these methods suffer from some common caveats, including the failure to characterise transitional and novel cell states, sensitivity to batch effects and under-utilisation of phenotypic information other than cell types (e.g. sample source and disease conditions).

We developed Φ-Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. In Φ-Space we adopt a highly versatile modelling strategy to continuously characterise query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualisations, clustering and cell type labelling.

We demonstrate through three case studies that Φ-Space (i) characterises develop- ing and out-of-reference cell states; (ii) is robust against batch effects in both reference and query; (iii) adapts to annotation tasks involving multiple omics types; (iv) over- comes technical differences between reference and query.

Patch 6.1.2 and some updates

R CRAN update

The new patch version of mixOmics is on CRAN. It includes a few bug fixes raised by our users (thank you!) and a few improvements. Florian Rohart has been fiddling really hard with ggplot2 to make a new plotIndiv version that can beautifully handle two legends!

plotIndiv example with two legends in 6.1.2

 

# indicate the group, treatment and pch for each sample
my.group
 [1] "group 1" "group 1" "group 2" "group 2" "group 3" "group 3" "group 4" "group 4" "group 1" "group 1" "group 2" "group 2" "group 3" "group 3" "group 4" "group 4"
[17] "group 1" "group 1" "group 2" "group 3" "group 3" "group 4" ....
my.treatment
 [1] "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" "trt 2" ....
my.pch.trt
 [1] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 16 16 ....

plotIndiv(pca.res, ind.names = F, title = 'PCA', legend = TRUE, 
 # legend 1 colors setting:
 group = my.group, col.per.group = color.per.group, legend.title = 'Groups', 
 # pch setting:
 legend.title.pch = 'Treatment', pch = my.pch.trt, pch.levels = my.treatment) 

Here is a list of the major bug fixes and improvements for 6.1.2:

New features:
————-
1 – tune.splsda now returns a ‘choice.ncomp’ which indicates the number of components to choose (only if nrepeat > 2, criterion based on t-tests)
2 – plotIndiv now enables two legends based on color, as well as pch, when pch is a factor different from what is indicated in group (use arguments pch and pch.levels, see ?plotIndiv)

Enhancements:
————-
1 – argument ‘cutoff’ now replaces ‘threshold’ in network for consistency with plotVar and circosPlot
2 – new argument ‘sd’ in plot.perf for block.splsda method
3 – new arguments “color.Y” and “color.blocks” in cimDiablo
4 – new argument ‘xlim’ in plotLoadings

Bug fixes:
———-
– directionality is now enforced in AUROC (results lower than 0.5 can be obtained, which would indicate a very poor model performance)

Manuscripts:

The MINT paper is out:

  • Rohart F.,  Matigian N., Eslami A., Bougeard S and Lê Cao, K. A.MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms. Now available on bioRxiv! in press in BMC Bioinformatics 18:128.

The mixOmics manuscript (first draft) is on bioRxiv, with sweave codes:

mixOmics 5.2.0 (graphical improvements)

6a010534b1db25970b01bb0794c2fc970d-800wi
The reality of R packages development. From http://www.r-bloggers.com/introducing-the-reproducible-r-toolkit-and-the-checkpoint-package/

 

We are proud to introduce a new mixOmics update dedicated mainly to improvements in graphical outputs. The changes are listed below, please note the change of arguments names (promise, we’ll try not do that again). More posts to come about the new functionalities.

We are particularly grateful to our key contributors Mr Francois Bartolo (Université de Toulouse, who is doing a short stay down here in Brisbane) and Dr Florian Rohart (University of Queensland) for doing such a great job with the development, debugging and testing. If we have missed something please let us know!

New features:
————-
1 – plotArrow for PLS, sPLS, rCC, rGCCA, sGCCA, sGCCDA is an improved version from our old s.match function (which is still available but will be soon deprecated)
2 – network function has been enhanced with various options to represent the nodes (e.g. lty.edge=’dotted’,row.names = FALSE), see our website for more examples
2 – rcc has a new argument method = c(“ridge”, “shrinkage”) with shrinkage to estimate the shrinkage coefficients directly
3 – plotIndiv directly implements 3d plots (style=’3d’), including ellipses, % of variance explained output for PCA, centroids and star plots (see example(plotIndiv))
4 – plotVar directly implements 3d plots (style=’3d’), legend can also be added with add.legend = TRUE
5 – cim and network have new arguments: save = c(‘jpeg’,’tiff’,’png’,’pdf’) to save plots directly, and name.save. Argument threshold has been added/updated for both displays. Some arguments underwent name changes, see ?network

Enhancements:
————-
1 – network: a single function for all objects.
2 – pheatmap.multilevel has been deprecated with the new enhancements of CIM
3 – plot3dIndiv and plot3dVar have been deprecated (see new features in plotIndiv and plotVar)
4 – plotContrib also now available for sgccda plsda, splsda objects. Added arguments coplete.name.var and col.ties (see ?plotContrib), changed argument name ties to show.ties
5 – imageMap has been deprecated (now included in cim directly)
6 – pca also outputs ‘loadings’ and ‘variates’ to remain in the mixOmics spirit
7 – tau.estimate help file removed as now directly called as internal function from rcc and srgcca
8 – imgCor: added argument ‘main’ and changed argument names x.sideColors and y.sideColors to sideColors
9 – cim: changed argument names labRow and labCol to row.sideColors and col.sideColors

Bug fixes:
———-
1 – plotContrib now fixed (showed wrong contribution colors)
2 – cim has been fixed to show the ordered variable names after users reports (thanks!)
3 – resolved blank page in network when saving image as a pdf